Diagnosis and prediction of in-field dry-down of a mature small grain, coarse grain, or oilseed crop using field-level analysis and forecasting of weather conditions, crop characteristics, and observations and user input of harvest condition states

ABSTRACT

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

FIELD OF THE INVENTION

The present invention relates to harvest operations in precisionagriculture. Specifically, the present invention relates to a system andmethod of applying real-time, field-level weather simulation andprediction to one or more agricultural models to generate a series ofharvest advisory outputs in a tool for supporting farm operationsmanagement.

BACKGROUND OF THE INVENTION

Harvest operations for a variety of agricultural commodities aresubstantially influenced by environmental factors, such as the weather.While some weather conditions, such as precipitation, may create anobvious deterrent to harvest operations, more benign daily weathercharacteristics also play subtle yet significant roles. For example,many commodities require that the harvested product be at or below aproduct-dependent moisture threshold before they can be stably stored atambient temperatures (at least without taking specific steps to keep theproduct stable, such as the maintenance of a constant airflow throughthe product). On the other extreme, delaying harvest for too long canresult in the crop becoming overly dry, potentially exposing seeds todamage during the threshing process, or removing permissible waterweight from the product. Such an occurrence of delayed harvest mayresult in lower crop revenue, since payments are often based on mass.Similarly, crop temperature thresholds may be a major consideration forlong-term storage of some crops, for example tuberous crops such aspotatoes and sugar beets, where cold conditions are advantageous.

The harvest operation itself is also often sensitive to plant, product,and soil moisture and temperature levels. For instance, green plants, oreven deceased plants with a heightened moisture level, often createdifficulty for harvest operations that are based on the use of athreshing action to separate the seed (or other product) from the parentplant. This can result in both yield loss due to un-threshed seedspassing through and out of the harvester, and seed damage due to therepeated or harsh threshing action that may be required. Frozen orexcessively wet soils can also inhibit harvest operations for variouscrops, depending upon the harvest mechanism for the particular crop.Each of the product, plant, and soil moisture and temperature thereforeimpact both the timing and viability of harvest operations, and all ofthese qualities are highly influenced by complex interactions betweenplant and soil characteristics and environmental conditions.

As global agricultural operations continue to grow in size, thepracticality of in-situ monitoring of field conditions on a regularbasis is becoming increasingly diminished. Further, the oftensubstantial equipment and labor resources involved in harvest operationsare not easily moved across significant distances in an effort to findfields with viable or more favorable harvest conditions. The ability toboth diagnose and predict the viability of harvest operations in apotentially remote field is therefore of increasing importance to themanagement of modern farm operations. Also, production agriculture isoften a capital-intensive business with very thin relative profitmargins. The ability to more effectively manage the logistics associatedwith deployment of a farm operation's equipment and human resources istherefore becoming increasingly critical to profitability and long-termviability of the farm itself.

In part because agricultural research globally is largely carried out byinstitutions with local or regional focus, agronomic models are oftenbased on sample datasets that are limited in size and/or geographicrepresentativeness, with less than ideal documentation of (oraccommodation for) associated weather and environmental conditions.Because of this, there are very few models that can be picked up andapplied to other locations and timeframes without a potentiallysubstantial loss in model accuracy. Models for the same processes canoften lead to diametrically opposed conclusions when applied atdiffering locations because of model shortcomings that are due to a lackof understanding of the extent to which localized influences impact theassociated processes during the development of the model.

Existing solutions do not provide a sufficient framework for utilizingweather analysis and prediction to accurately diagnose field-levelweather conditions for precision agriculture to overcome the challengesabove. Accordingly, there is a strong need not found in the existing artfor a system and method that provides an improved process forapplication of weather information in agronomic modeling to produce abetter understanding of farm and harvest operations. There is also aneed not found in the existing art for support tools designed to providereal-time assessments of weather conditions and the impact on crops,plants, soils, and resulting agricultural products to enableimprovements in farm and harvest operations.

BRIEF SUMMARY OF THE INVENTION

It is therefore one objective of the present invention to provide asystem and method of applying real-time assessments of weatherconditions to precision agriculture models for enhancing harvestoperations. It is another objective to combine real-time andlocation-tagged data communication in farm operations with suchreal-time assessments of weather conditions for generating harvestadvisory information in precision agriculture. It is still anotherobjective of the present invention to combine user-provided and/orobserved feedback of a present state of a harvest-related condition withreal-time assessments of weather conditions for generating harvestadvisory information in precision agriculture. It is yet anotherobjective of the present invention to provide a diagnostic support toolfor enhanced decision-making in harvest operations.

There are many other specific objectives of the present invention. Onesuch objective is to provide a system and method of predicting dry-downof a crop over time, such as a mature small grain, coarse grain, oroilseed crop, for planning the timing of harvest operations. It isanother such objective of the present invention to provide a system andmethod of predicting the time-varying unit costs, per percent moistureper unit of mass or volume, associated with fuel-based or forced-airmechanical drying of crops resulting from changing weather conditionsand the characteristics of the drying facility. Yet another objective ofthe present invention is to provide a system and method of predictingthe time-varying unit costs, per unit of mass or volume, associated withthe combined impacts of time-varying grain moisture levels of a crop tobe harvested and the time-varying unit costs per percent moisture perunit of mass or volume of forced-air or fuel-based mechanical drying.

It is another objective to provide tools for assessing a risk of delayedharvest operations to achieve favorable crop moisture levels, forevaluating soil temperature and soil moisture in a given field toprovide guidance as to when soil conditions are likely to inhibitharvest and post-harvest tillage operations due to soil beingexceedingly wet or frozen, and for evaluating the impact of time-varyingsoil temperature in a given field for the timing of harvest operationsas it pertains to the internal temperature of root-based crops and theirlong-term storage stability at those temperatures.

Other objectives of the present invention include providing systems andmethods for predicting the day-by-day and intraday windows ofopportunity for harvest operations owing to time-varying threshabilityas a result of interactions between a standing crop and atmospheric andsoil conditions, to aid in more immediate planning of harvestoperations; predicting the dry-down of hay (alfalfa, etc.) and othercuttings over time for planning the timing or windows of opportunity ofcutting, windrowing, silageing, or bailing operations; and assisting indecision-making for application of a desiccant to affect a desiredharvest window.

The present invention is a system and method of evaluating, diagnosing,and predicting various agronomic conditions attendant to farmingactivities in an advisory model configured to simulate and predict theimpact of weather conditions on harvest operations. The presentinvention applies real-time, field-level data representative ofassessments of localized weather conditions, together with real-time andlocation-tagged communication of data of various types and content, andlong-range climatological and/or meteorological forecasting, to one ormore physical, empirical, or artificial intelligence models of precisionagriculture to analyze crops, plants, soils, and resulting agriculturalcommodities, and generate a plurality of harvest advisory outputs. Suchoutputs are provided either directly to farmers, to third parties, or ina harvest advisory tool based on the advisory model for supportingprecision agricultural operations.

Through application of the harvest advisory modeling paradigms discussedherein, the prospects for providing improved guidance relating to farmand harvest operations has the potential to be very substantial in thefield of precision agriculture. For example, diagnosing and predictingthe in-field dry-down of agricultural commodities, the efficiency offuel-based and/or forced-air grain drying activities, the associationsbetween plant moisture and atmospheric conditions, and the soiltemperature and moisture profiles all provide significant value-addedbenefit. Recent parallel advances in weather analysis and prediction,and in the availability of mechanisms for facilitating real-time andlocation-tagged data communication in farm operations, have created anenticing set of possible new applications for addressing the problemsdescribed above, and others. The application of both in-situ (though notnecessarily in or near a given field) and remotely-sensed weatherinformation, in combination with scientific and computational advancesin the integration of data collected by these disparate weatherobserving systems, permit the diagnosis of field-level weatherconditions with accuracy that greatly improve upon that obtained withthe deployment of a basic weather station to each and every field.

Further, advances in the understanding of the interactions between theland surface and the overlying atmosphere, combined with otherimprovements to the physics of meteorological weather models, and theever-increasing computational power available to operate these models,enable a level of both short-term and long-term accuracy and locality toweather forecasts that has not been previously attainable.

The present invention also applies time- and location-tagged feedbackand observations on current and recent characteristics representingmeasurements of various crop states, such as moisture and temperature,to the one or more physical, empirical, or artificial intelligencemodels of precision agriculture. Such feedback and observations providean enhanced collection of data along with the associated weatherconditions and other relevant information to build more comprehensivedatasets that are used to make far-reaching improvements to associatedphysical models for diagnosing and predicting underlying harvest-relatedconditions. Artificial intelligence is also incorporated to this morecomprehensive dataset to draw automatic associations between availableexternal data and the harvest-related condition to yield further modelsfor simulating harvest conditions. Artificial intelligence in thepresent invention is also retrained as more and more data areaccumulated, and the results may be tested against independent data inan effort to find the most reliable model. Such a model frameworkimplicitly yields information as to the importance of related factorsthrough the resulting weighting systems between inputs, subcomponentswithin the artificial intelligence layer, and the model output(s).Together, this feedback and observations, and physical, empirical, andartificial intelligence modeling paradigms discussed hereinsignificantly enhance the analysis of crops, plants, soils, andresulting agricultural commodities within the present invention togenerate harvest advisory outputs and improved guidance for farm andharvest operations.

Other objects, embodiments, features and advantages of the presentinvention will become apparent from the following description of theembodiments, taken together with the accompanying drawings, whichillustrate, by way of example, the principles of the invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a systemic diagram of a harvest advisory model and supporttool for according to the present invention; and

FIG. 2 is a block diagram of information flow and steps performed withina harvest advisory model and support tool according to the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the present invention reference is madeto the exemplary embodiments illustrating the principles of the presentinvention and how it is practiced. Other embodiments will be utilized topractice the present invention and structural and functional changeswill be made thereto without departing from the scope of the presentinvention.

FIG. 1 is a system diagram of a harvest advisory model 100 forevaluating, diagnosing, and predicting various agronomic conditions thathave an impact on farm and harvest operations, according to oneembodiment of the present invention. The harvest advisory model 100 isperformed within one or more systems and/or methods that includesseveral components, each of which define distinct activities required toapply real-time, field-level data representative of assessments oflocalized weather conditions, together with real-time andlocation-tagged communication of data of various types and content, andlong-range climatological and/or meteorological forecasting to analyzecrops, plants, soils, and agricultural products, to generate a pluralityof harvest condition advisory outputs, and enable a harvest advisorytool 200 configured to provide diagnostic support to farm and harvestoperations.

The harvest advisory model 100 ingests, or receives, input data 102 thatincludes weather and location specifications 110, such as meteorologicaldata 111, real-time, field-level weather data 112, observed weather data113 that has occurred, for example, during a current growing season, andextended-range weather and climatological forecast information 114. Theweather and location specifications 110 may also include other types ofreal-time and location-tagged data 115, such as for example GPS data116, and data captured from orbiting satellites or remotely poweredvehicles that when processed provide details at a field-levelresolution, such as remotely-sensed satellite imagery data 117 andremotely-captured drone imagery data 118. The input data 102 may furtherinclude crop and plant specifications 120 such as for example the type,variety and relative maturity of the crop 121, and other planting,chemical application, and harvest data 122, such as for example the datethat a field was planted with seed, the population of planted seeds perspecified area, when a field was sprayed for example with a pesticide,desiccant, other chemical, the type and amount of chemical applied, andanticipated temporal harvest data such as an expected harvest date orharvest window. The crop and plant specifications 120 may also includethe desired crop temperature 123, crop moisture 124, seed moisture 125,facility storage dimensions and specifications 126, and airflowcharacteristics for a stored crop 127. Additional information mayinclude planting information such as plant depth 128 and row width 129.

The input data 102 may also include field and soil specifications 140,which may further include data such as for example the soil type 141 inwhich a crop was planted, and attributes such as soil temperature 142,soil moisture 143, current and/or forecasted soil conditions 144,overall soil profile 145, levels of vegetative debris 146 existing atthe time of planting, and nutrient levels 147 present.

The input data 102 may also include physical, empirical, or observedagricultural data 130, such as for example sampled crop moisture content131. This may include data such as samples and/or observations of actualgrain moisture taken from a planted field at one or more times, and theassociated dry-down of the grain in a particular field over time. Otherexamples of such data 130 include data regarding temporal harvestabilitywindows 132 for a particular crop at a particular location over somerecent period of time, or for various crops at various locations, overtime. This may include, for example, temporal windows of threshabilityof a seed-based crop over some recent period of time, temporal windowsof cut-windrow-bail operations for crops such as hay at a particularlocation over some recent period of time, and temporal windows of maturegrowth stages, desiccant efficacy, and harvestability for a particulardesiccant for particular crop at a particular location over some recentperiod of time.

The physical, empirical, or observed agricultural data 130 may furtherinclude descriptive metadata reflecting actual performance, realization,or implementation of one or more of crops, plants, fields, soils, andfacilities. This metadata may include crop and seed metadata 133 such asthe relative maturity and dry-down characteristics of the variety, pestand disease susceptibility or resistance, whether a crop is irrigated ornon-irrigated, and type and origin of seeds (for example, geneticallymodified or non-genetically modified, and company source). Such metadatamay also include soils metadata 134 such as information relating toprevious crop grown on or in the soil, tillage practice, soilcomposition, presence of surface and/or subsurface drainage, nutrientcomposition, rate of degradation, rate of water movement in both alateral and vertical direction, presence of materials such as salts insoil strata, and facility metadata 135 such as metadata relating to typecharacteristics, for example whether the facility is a forced-air dryingfacility or a fuel-based mechanical grain drying facility, and theirassociated resources and costs.

All of this input data is ingested into the harvest advisory model 100via a data ingest module 171 as shown in the system diagram of FIG. 1.The harvest advisory model 100 ingests this input data 102 and appliesit to one or more precision agriculture (agronomic) models 160 which mayinclude artificial intelligence-derived precision agriculture models, toproduce output data 180. The output data 180 of the harvest advisorymodel 100 is represented a harvest condition output profile 181 that isprovided to a harvest advisory tool 200 that can be used to predict,simulate, and forecast harvest conditions 270 as discussed furtherherein.

The input data 102 is applied to the one or more precision agriculturemodels 160 in a plurality of data processing modules 170 within acomputing environment 150 that also includes one or more processors 152and a plurality of software and hardware components. The one or moreprocessors 152 and plurality of software and hardware components areconfigured to execute program instructions to perform the functions ofthe harvest advisory model 100 described herein, and embodied in the oneor more data processing modules 170.

The plurality of data processing modules 170 include, in addition to thedata ingest module 171, a physical and empirical models module 172, andan artificial intelligence module 173 configured to apply a layer ofartificial intelligence to the harvest advisory model 100 to developrelationships between input data 102 and outputs from other precisionagriculture models to perform and improve the assessments, diagnoses,simulations, forecasts, and predictions that form the harvest conditionoutput profile 182. The plurality of data processing modules 170 alsoincludes a harvest condition prediction module 177 configured togenerate the harvest condition outputs 270.

The physical and empirical models module 172 enables application of theinput data 102 to the one or more precision agriculture models 160. Suchmodels 160 include crop, soil, plant, and other modeling paradigms, suchas for example phenological models that include general crop-specificand crop variety-specific models, a common example being growing degreeday (GDD) models. These models 160 also include soil models such as theEPIC, APEX, and ICBM soil models, and land surface models such as theNOAH, Mosaic, and VIC models. Other models contemplated within the scopeof the present invention include crop-specific, site-specific, andattribute-specific physical models, such as models for simulatingtemperature, moisture, wetness, in-field crop dry-down, plant-atmospherevapor diffusion, plant rewetting associated with precipitation, anddeterministic and stochastic grain drying models that parameterize theunique drying characteristics of a specific crop to be dried based oncharacteristics of the drying process and statistical models, and modelsfor analyzing storage characteristics of crops, seeds, soils, andstorage facilities. It is contemplated that the input data 102 may beapplied to existing precision agriculture models, as well as customizedmodels for specific harvest conditions 270, for example simulation ofexpected dry-down of grain in the particular field, grain and plantmoisture levels, and temperature and moisture content of a root-basedcrop over time, and other simulations, predictions, and forecasts asnoted herein.

The harvest condition output profile 182, as noted above, is output data180 of the harvest advisory model 100. Once the harvest advisory model100 has generated the harvest condition output profile 182, the presentinvention applies the harvest condition output profile 182 to developthe harvest advisory tool 200 for analyzing many types of information inharvest operations. The harvest condition output profile 182 providesestimates, for example, of standing crop dry-down rates, anticipatedharvest dates and suitability, fuel consumption optimizers forforced-air drying, indicators of plant ‘toughness’ for anticipatingharvest windows, and possible loss of field workability due to theformation of frost in the soils prior to post-harvest tillage.

FIG. 2 is a block diagram of information flow and steps performed withinthe harvest advisory model 100 and harvest advisory tool 200 forsupporting users 300. In the present invention, the model 100 isinitialized 210 by receiving various input data 102, such as weather andlocation specifications 110. Crop and plant specifications 120 may alsobe applied at this phase. The harvest advisory model 100 uses thisinformation to develop predictions of expected weather conditions 230using one or more weather modeling paradigms. Additional input data 102related to specific harvest conditions 270 to be modeled may then beapplied, such as seed, soil, field and facility data, and any type ofmetadata. The present invention may utilize one or more predictive datamodels 175 for generating predictions or forecasts 220 relating to suchadditional input data 102, depending on the type of harvest condition270.

The expected weather conditions 230, and additional input data 102, arethen applied in step 240 to one or more precision agriculture models160. Output from such models 160 may also be further applied to develop242 artificial intelligence models for further analysis of the harvestcondition 270, as discussed further herein. The present inventionperforms an initial evaluation 250 of the harvest condition 270, and mayingest input data 102 representing further observations to performmodifications 260 of the initial evaluation. The present invention thenproduces the harvest condition output profile 182 and provides this tothe harvest advisory tool 200, for use by one or more users 300 or forfurther distribution or processing in an API module 176.

As noted above, the harvest advisory model 100 includes a layer ofartificial intelligence that may be applied to develop relationshipsbetween input data 102 and outputs from others of the precisionagriculture models to perform the simulations, forecasts, diagnoses, andpredictions represented in the harvest condition output profile 182. Thepresent invention contemplates that these relationships may be developedin a variety of different ways. For example, the artificial intelligencemodule 173 is configured to adjust to input data 102 that includesobservations provided from fields that are local or even distributedacross a region. Other data applied to the artificial intelligencemodule 173 includes, as noted above, output from other precisionagriculture models, and it is further contemplated that the originaldatasets for such models may be collected from institutions around theworld and analyzed within the present invention. Regardless,observations may be either quantitative (e.g. grain moisture level astested) or qualitative (e.g. the plant material became harvestable orunharvestable at a given time and location, whether or not because ofenvironmental factors). Such information can be analyzed with respect toboth recent and current weather and soil conditions, and thecorresponding agronomic model outputs, to make appropriate adjustmentsto the unaltered agronomic model output that would otherwise be providedto the user. This information can also be used to provide updatedguidance to other users in similar circumstances (with respect tolocale, crop, etc.).

The artificial intelligence module 173 uses associated weatherconditions, agronomic and other agricultural model outputs,environmental considerations, crop characteristics, and other relevantdata, together with empirical observations where applicable, to build amore comprehensive dataset that can be used to make far-reachingimprovements to (or even replacements for) the associated physicalmodels for diagnosing and predicting the underlying harvest-relatedcondition. For instance, the artificial intelligence layer can beapplied to an adequately-sized dataset to draw automatic associationsbetween the available external data and the harvest-related condition,effectively yielding a ‘black box’ model for simulating the condition.As more and more data are accumulated, the data can be sub-sampled, theartificial intelligence layer retrained, and the results tested againstindependent data in an effort to find the most reliable model. Further,such a model implicitly yields information as to the importance ofrelated factors through the resulting weighting systems between inputs,subcomponents within the artificial intelligence layer, and the modeloutput(s). This information may be used to identify which factors areparticularly important or unimportant in the associated process, andthus help to target ways of improving the model over time.

The present invention contemplates that many different types ofartificial intelligence may be employed within the scope thereof, andtherefore, the artificial intelligence module 173 may include one ormore of such types of artificial intelligence. The artificialintelligence module 173 may apply techniques including, but not limitedto, k-nearest neighbor (KNN), logistic regression, support vectormachines or networks (SVM), and one or more neural networks.

The use of artificial intelligence in the harvest advisory model 100 ofthe present invention enhances the utility of physical and empiricalmodels by automatically and heuristically constructing appropriaterelationships, mathematical or otherwise, relative to the complexinteractions between growing and maturing plants, the environment inwhich they reside, the underlying processes and characteristics, and theobservational input data made available to the system. For example,where predictive factors known to be related to a particular outcome areknown and measured along with the actual outcomes in real-worldsituations, artificial intelligence techniques are used to ‘train’ orconstruct a model that relates the more readily-available predictors tothe ultimate outcomes, without any specific a priori knowledge as to theform of those relationships.

The present invention adopts a combined approach for simulating therelationships between predictive data and eventual outcomes. In theharvest advisory model 100, the general nature of the relationships canbe quantified with one or more physical models and/or with artificialintelligence models, which are then applied to a combination of thepredictive data, historical data, metadata, and other physical modeloutputs to better simulate the ultimate outcomes. This approach permitsthe better-understood portions of the problem at hand to be modeledusing a physical or empirical model, while permitting the less wellunderstood portions of the problem to be automatically modeled based onthe relationships implicit in the data provided to the system. Inadditional embodiments, with sufficient input data and outputreliability and accuracy, the physical models may be entirely supplantedby the use of artificial intelligence model(s). Alternatively, theartificial intelligence layer need not be employed in the system toproduce the desired output information.

The proliferation of devices for communicating data both collected andrequired during harvests provides the opportunity to address many issueswith obtaining reliable, centralized datasets for modeling harvestactivities. For example, issues arise because, although key predictivefactors or ground truths are routinely measured in productionagriculture, they are rarely reported into a centralized repository ofdata that could be used to develop models that simulate the relevantrelationships. Further, the mere act of reporting and collecting thisdata does not in itself provide the ability to develop models based onthe data. In one embodiment of the present invention, past, present, andpredicted weather data for any location globally are constructed throughcombined application of one or more of in-situ weather observations,remotely-sensed weather observations, and meteorological analysis andprediction models. These data are made available via an API-based systemfor obtaining weather data for any given location and timeframe.Likewise, global, national, and regional databases of soil andland-cover characteristics are also made available via an API system,making these potentially predictive parameters readily available forassociation with any harvest-related observations that might becomeavailable. These data, along with user-provided crop metadata, can beused to diagnose and predict the growth and maturation of a crop to beharvested, with or without the aforementioned systems and methodsincluding whether or not the processes are performed on a mobile device.Using a field data collection device, such as a smartphone or tablet,observations of conditions impacting harvest operations can be providedin near real-time. These observations may include grain moisture samples(whether from a trial harvest operation or manual sampling), cropharvestability metrics, field accessibility metrics, or any number ofother characteristics of the crop or field that may impact the crop'sbehaviors or its harvest operations. Additionally, through combinedapplication of locational information available from the device, andcrop or field metadata relating to the aforementioned observations thatmight be collected through an application accessible on the device, dataon additional predictive factors can be collected and associated witheach observation. When combined with automatically-collected weather,soil, and other environmental data, these data can be used as the basisfor automatically building artificial intelligence models that eithersimulate the future expected states of the harvest-impacting conditionsdirectly, or that act to provide corrective adjustments to the outputsof one or more physical models for simulating the underlying processes.

The systems and methods of the present invention therefore provide theopportunity to substantially advance the state of the art in terms ofplanning and managing harvest operations with the use of combinations ofphysical and empirical models with artificial intelligence. It isfurther notable however that many types and sources of input data 100allow for user-based and/or locality-based biases in the observationsand predictive data that are available to develop the artificialintelligence modeling paradigms contemplated herein. Some of thesebiases may represent differences in perception (where subjectivefeedback is accepted), while others may be due to biases in theinstrument(s) used to collect more quantitative observations, and evenothers may be due to variability in factors associated with the crop orfarm operation that are outside of the realm of what is being collectedin terms of metadata (for instance, the design of the particularharvesting equipment used can impact both harvestability andfield-accessibility for a given operation). Because of this, the presentinvention contemplates development of both generalized artificialintelligence models, using all available data and metadata, andlocalized or user-specific artificial intelligence models tailored toanalyze, for example: a particular location, user, crop, operation, soiltype, and seed variety. These highly-localized or -personalized modelsmay yield information that is more closely-tailored to the particularlocation or user than that provided from original data.

As noted above, the harvest advisory model 100 ingests many differenttypes of weather information for application to the modeling approachesdiscussed above. This includes real-time, historical, and forecastedfield-level data representative of assessments of localized weatherconditions, and may include long-range climatological and/ormeteorological forecasting, in one or more predictive weather models174.

Such real-time, field-level data representative of assessments oflocalized weather conditions may be produced by many different sourcesof meteorological data to provide one or more of observed weather dataand current field-level weather data, and predicted weather data, forexample as data that is complementary to the data assimilation systemsand forecasting systems noted below. Such additional sources of weatherdata may include data from both in-situ and remotely-sensed observationplatforms. For example, numerical weather prediction models (NWP) and/orsurface networks may be combined with data from weather radars andsatellites to reconstruct the current weather conditions on anyparticular area to be analyzed. There are numerous industry NWP modelsavailable, and any such models may be used as sources of meteorologicaldata in the present invention. Examples of NWP models at least includeRUC (Rapid Update Cycle), WRF (Weather Research and Forecasting Model),GFS (Global Forecast System) (as noted above), and GEM (GlobalEnvironmental Model). Meteorological data is received in real-time, andmay come from several different NWP sources, such as from the EuropeanCentre for Medium-Range Weather Forecasting (ECMWF), MeteorologicalServices of Canada's (MSC) Canadian Meteorological Centre (CMC), as wellas the National Oceanic and Atmospheric Administration's (NOAA)Environmental Modeling Center (EMC), and many others. Additionally,internally or privately-generated “mesoscale” NWP models developed fromdata collected from real-time feeds to global and localized observationresources may also be utilized. Such mesoscale numerical weatherprediction models may be specialized in forecasting weather with morelocal detail than the models operated at government centers, andtherefore contain smaller-scale data collections than other NWP modelsused. These mesoscale models are very useful in characterizing howweather conditions may vary over small distances and over smallincrements of time. The present invention may be configured to ingestdata from all types of NWP models, regardless of whether publicly,privately, or internally provided or developed.

Long-range climatological and/or meteorological forecasting may begenerated by, in one aspect of the present invention, dynamical extendedrange weather forecast models generated from known methods forinter-seasonal to inter-annual climate prediction, which have evolvedinto a combination of deterministic and statistical modeling schemesthat permit the capture of long-term low-frequency features. Suchforecasting often requires global analyses for the specification ofobserved initial and boundary conditions, the use of sophisticatednumerical weather prediction models, a statistical treatment of themodel results, and a verification of the forecast results as a feedbackinto forecast refinement.

In one embodiment of the present invention, providing long-rangeclimatological and/or meteorological forecasting may involve the use oftwo data assimilation systems and two forecasting systems. The two dataassimilation systems may be used to provide historic and currentatmospheric and land surface initial conditions and also global oceantemperatures. For example, the two forecast systems may incorporate theU.S. National Centers for Environmental Predictions (NCEP) GlobalForecast System (GFS) for atmospheric predictions and the GeophysicalFluid Dynamics Laboratory Modular Ocean Model to provide sea-surfacetemperature predictions. Software and data supporting the above arepublicly available from the NCEP.

The present invention contemplates that one or more predictive weathermodels 174 may be incorporated to develop expected weather conditions230 as in FIG. 2. Such models may be executed at any desirable timeinterval, such as daily or hourly, to provide a complete global datasetfor use in initializing the subsequent model run, and to be used tosupply localized values for harvest projections for individual croplocations.

Another type of input data 102 contemplated within the scope of thepresent invention is image data, such as field-level processedremotely-sensed imagery in the form of remotely-sensed satellite imagerydata 117 and remotely-captured drone imagery data 118, which may beingested to provide additional support for an assessment of crop andsoil states and yield metrics in the harvest advisory model 100. Onesource of image data representing this remotely-sensed imagery issatellite systems, such as fine temporal resolution low-earth orbitsatellites that provide a minimum of three spectral bands. Anothersource is unmanned or remotely-piloted vehicles such as those commonlyreferred to as “drones”. Other sources are also contemplated, such asfor example manned aerial reconnaissance, lower temporal frequency earthresources satellite such as LANDSAT and MODIS, ground-based robots, andsensors mounted on field and farm equipment. Regardless of the source,this image information is field-navigated to provide users with the mostrecent high-resolution depiction of a field to be or being harvested.Image data may be delivered on a web or application-based deviceconfigured for use with the harvest advisory tool 200, and additionaltools may be provided for spatially navigating the image data andoverlaying a variety of weather data elements.

Regardless of the source, the field-level remotely-sensed raw or imagedata may be used by the harvest advisory model 100 to map the crop fieldand generate a time-series profile of harvest activity. It iscontemplated that remotely-sensed satellite imagery data 117 andremotely-captured drone imagery data 118 may be analyzed prior to or inconcert with application to the one or more modeling paradigms discussedherein. The remotely-sensed satellite imagery data 117 andremotely-captured drone imagery data 118 may be analyzed using anormalized difference vegetative index (NDVI) that provides the userwith an evaluation of plant health, biomass, nutrient content andmoisture or wetness content. Other approaches may also be employed forsuch analysis, such as: Modified Chlorophyll Absorption RatioIndex/Optimized Soil-Adjusted Vegetation Index (OSAVI), TriangularChlorophyll Index/OSAVI, Moderate Resolution Imaging SpectrometerTerrestrial Chlorophyll Index/Improved Soil-Adjusted Vegetation Index(MSAVI), and Red-Edge Model/MSAVI.

The harvest advisory model 100 may further include additional sources ofdata, such as image-based data from systems such as video cameras, anddata generated from one or more vehicle-based sensing systems, includingthose systems coupled to computing systems configured on harvestingequipment, or those systems configured to gather weather data frommobile devices present within vehicles, such as the mobile telephonydevices and tablet computers as noted above. Crowd-sourced observationaldata may also be provided from farmers using mobile telephony devices ortablet computers using software tools such as mobile applications, andfrom other sources such as social media feeds. Meteorologist input maybe still a further source of data. The data collected or used need notbe in image format.

Visible adaptation of the output of any of the modeling paradigmsemployed within the harvest advisory model 100 and harvest advisory tool200 to user-provided feedback data helps to foster a productive andongoing feedback loop between the user 300 and the harvest advisorymodel 100 over time. While adding an artificial intelligence layer tothe overall modeling system leads to output that is generally responsiveto user feedback on the whole, it is possible that even the output ofthe artificial intelligence model (or artificial intelligence layerfunctioning together with a physical model) may differ from real-worldobservations. The present invention therefore contemplates, in anotherembodiment, an extra layer of forced-adaptation software for adjustingoutput to ensure responsiveness to feedback data and enforce the beliefthat data being provided is actually being used to automatically improvethe corresponding model over time. Since even a model based onartificial intelligence cannot be reasonably forced to return an outcomethat is consistent with what is observed, even after that observationhas been included in the pool of data that was used to develop the modelin the first place, a forced-adaptation layer that varies according tothe nature of the problem and model(s) to which it resides is applied sothat information provided to the user going forward is well-matched tothe information the user has provided. For example, it is entirelypossible that feedback and observational data provided by two differentusers, for seemingly identical situations from the perspective of thedata the system has available, will conflict, even to the extent ofpotentially being diametrically opposed. Applied to the same adaptivelearning model, the present invention may produce an indeterminateoutcome because of the conflicting feedback, or even a conflictingoutcome to what the user has reported. The forced adaptation layer istherefore an additional module atop the artificial intelligence layerthat provides an appropriate albeit brute-force adjustment to systemoutput to ensure that it is consistent with the feedback andobservational data the user has provided for the appropriate timeframe(if one exists), so as to provide the perception of responsiveness tothe user and encourage the provision of further feedback andobservational data.

The present invention contemplates that many applications of the harvestadvisory model 100 are used to provide specific output information forsupporting farming and harvesting operations. In one such application,the harvest condition output profile 182 provides a pre-harvestprediction of in-field dry-down of a mature small grain, coarse grain,or oilseed crop over time, but crop type need not be limited to this.For example, root-based crops may be considered for either in-field cropdry-down or crop moisture acquisition. This prediction is provided viathe harvest advisory tool 200 and is used for planning the timing ofpre-harvest, harvest, and post-harvest operations for such crops.

The harvest advisory tool 200 provides this prediction in one or moreapproaches. In one such approach, the harvest advisory model 100leverages weather prediction models to predict weather conditions thatimpact the rate of drying or wetting of a root-based crop or the grainassociated with a mature small grain, coarse grain, or oilseed crop in aparticular field. A physical model is also applied to simulate theexpected dry-down of the grain in the particular field, based on thecombination of crop and field characteristics and the expected weatherconditions. This is accomplished by receiving one or more samples ofactual grain moisture from the field at one or more times, andmodifying, based on the differences between simulated and sampledmoisture content at the corresponding times, the prediction of theexpected dry-down of the grain in the particular field over time.

In another approach, a prediction of weather conditions that impact therate of drying or wetting of a root-based crop or the grain associatedwith a mature small grain, coarse grain, or oilseed crop in a particularfield is formed using crop metadata, and one or more samples of actualgrain moisture from one or more fields at one or more times. The weatherconditions, crop metadata, and root or grain moisture samples from oneor more fields and one or more crops are applied to automaticallydevelop an in-field root or grain dry-down model based on artificialintelligence. The harvest advisory model 100 then applies the artificialintelligence-based model to diagnose or predict the in-field dry-down ofgrain with similar characteristics in any field at any chosen time.

In another application, the harvest condition output profile 182provides a prediction of the time-varying unit costs (per percentmoisture per unit of mass or volume) associated with fuel-based orforced-air mechanical drying of grain resulting from changing weatherconditions and the characteristics of a grain drying facility. Thisprediction is provided via the harvest advisory tool 200 for evaluatingthe impact of time-varying weather conditions on the unit costs offorced-air or fuel-based drying.

In this application, the harvest advisory model 100 leverages weatherprediction models to predict weather conditions that impact the energyrequirements associated with at least one of forced-air and fuel-basedmechanical grain drying. This is accomplished by receiving metadata(whether past, present, or future) regarding the characteristics of atleast one of forced-air and fuel-based mechanical grain drying facilityand their associated resources, and applying the predicted weather dataand facility metadata in at least one of an empirical, physical, andartificial intelligence model to predict the time-varying cost of atleast one of forced-air and fuel-based grain drying. Factors that mayimpact the cost of forced-air or fuel-based drying include theatmospheric temperature and moisture content, the temperature of thegrain itself (often reflective of the ambient temperature at the time ofharvest), the moisture of the grain, the amount of airflow with respectto a measurement of area or volume, cost of the energy used to drive theairflow, and the energy efficiency of the facility itself. In theprocess of drying heat is necessary to evaporate moisture from the grainand a flow of air is needed to carry away the evaporated moisture. Thereare two basic mechanisms involved in the drying process; the migrationof moisture from the interior of an individual grain to the surface, andthe evaporation of moisture from the surface to the surrounding air. Therate of drying is determined by the moisture content and the temperatureof the grain and the temperature, the (relative) humidity and thevelocity of the air in contact with the grain. Likewise, facilitydesign, such as continuous-flow dryers, batch dryers, tower dryers, andthe like can impact the ultimate drying costs by 50% or more.

In yet another application, the harvest condition output profile 182provides a prediction of the time-varying unit costs (per unit of massor volume) associated with the combined impacts of time-varying grainmoisture levels of the crop to be harvested, and the time-varying unitcosts (per percent moisture per unit of mass or volume) of forced-air orfuel-based mechanical drying. This prediction is provided via harvestadvisory tool 200 for evaluating the costs associated with in-field vs.facility-based drying of an agricultural commodity requiringsufficiently low moisture levels for stable long-term storage.

In this application, the harvest advisory model 100 predicts thein-field dry-down of the grain associated with a crop in a particularfield using one or more physically-based and/or or artificialintelligence models to simulate the combined impacts of weatherconditions, crop characteristics, and field characteristics on grainmoisture levels. The harvest advisory model 100 then uses predictedweather data to predict metadata associated with one or more offorced-air and fuel-based mechanical grain drying facilities, andanalyzes relationships that quantify the process of grain drying, theper-unit, time-varying cost of drying the grain (cost, per percentagepoint of moisture, per unit of grain mass or volume). Then, using thepredicted in-field grain dry-down and the predicted cost of one or moreof forced-air and fuel-based grain drying, the effective time-varyingoverall cost of forced-air and/or fuel-based grain drying (cost per unitof grain mass or volume) is predicted.

Another application of the present invention includes aiding in theassessment of the expected costs of forced-air or fuel-based mechanicaldrying of a crop owing to early harvest, relative to the potential costsof crop losses due to adverse weather conditions and the susceptibilityof the in-field crop to weather and other causes of yield loss. In thisapplication, the harvest condition output profile 182 provides anassessment of the trade-offs associated with delaying harvest operationsto achieve a more appealing crop moisture level verses the risksassociated with leaving the crop exposed in a field. The presentinvention performs this assessment by applying predictions of thein-field grain dry-down and the time-varying cost of at least one offorced-air and fuel-based grain drying to predict the effective overalltime-varying cost of mechanical forced-air or fuel-based grain drying(cost, per unit of grain mass or volume). It then predicts, using cropcharacteristics and predicted weather data, the time-varying potentialfor crop yield losses owing to adverse weather conditions, and providestime-varying comparative data on the costs of at least one of forced-airand fuel-based drying associated with earlier harvest of the crop,versus the risks associated with yield losses associated with adverseweather conditions that may be experienced prior to harvest of the crop.

The present invention may also be configured to diagnose and predict thesoil temperature and moisture profiles in a given field to provideguidance as to when soils in that field are likely to become exceedinglywet or frozen, thereby inhibiting harvest and post-harvest tillageoperations. In this application, the harvest condition output profile182 provides an assessment of the trade-offs associated with delayingharvest operations to achieve a more appealing crop moisture levelverses the risks associated with leaving the crop exposed in a field.The present invention performs this assessment by applying predictionsof the in-field grain dry-down and the time-varying cost of at least oneof forced-air and fuel-based grain drying to predict the effectiveoverall time-varying cost of mechanical forced-air or fuel-based graindrying (cost, per unit of grain mass or volume). In parallel it alsopredicts, using crop characteristics, soil data, and predicted weatherdata, the time-varying likelihood of at least one of excess moisture orfrost developing within the soil profile, and provides time-varyingcomparative data on the costs of forced-air or fuel-based dryingassociated with earlier harvest of the crop, versus the risks associatedwith excessive moisture or frost developing within the soil profileprior to the time when at least one of harvest operations andpost-harvest tillage operations can be completed.

In another application, the harvest condition output profile 182 enablesa diagnosis and prediction of the impacts of time-varying soiltemperature profile in a given field as it pertains to the internaltemperature of root-based crops and their long-term storage stability atthose temperatures. This is provided via harvest advisory tool 200 foraiding in the timing of harvest operations so as to optimize storageconditions.

The present invention provides this diagnosis and prediction in one ormore approaches. In one approach, the harvest advisory model 100leverages at least one of weather prediction models, land surface, andsoil models to predict the time varying temperature and moisture contentof a root-based crop intended to be harvested. Utilizing predictedweather conditions, the present invention then predicts, using the croptemperature and moisture, and airflow characteristics of the storagepile or facility, the stability of the crop for long-term storage.

In another approach, the present invention collects data on croptemperature and moisture, storage dimensions, and airflowcharacteristics for a stored crop, over time. It then compares this cropstorage data against at least one of time-varying weather data andoutputs of models for simulating crop moisture and temperaturecharacteristics in the corresponding times and locations, for thecorresponding crops, to develop artificial intelligence models forrelating these crop storage characteristics to the morereadily-available weather data and outputs of models for simulating cropmoisture and temperature characteristics. Using the airflowcharacteristics of the storage pile or facility, and the artificialintelligence model and at least one of forecast weather data andforecast outputs of models for simulating crop temperature and moisturecharacteristics, the present invention then provides a forecast of thelikely windows of harvestability on the current and future days and thepotential for crop stability for long-term storage.

In another application, the harvest condition output profile 182 enablesprediction of the day-by-day windows of opportunity for harvestoperations owing to time-varying threshability as a result ofinteractions between a standing or windrowed crop and atmospheric andsoil conditions. The present invention provides this prediction to aidin more immediate planning of harvest operations in one or moreapproaches. In one approach, the harvest advisory model 100 predicts thedaily window of harvest opportunity due to the impact of plant wetnesson crop threshability by collecting data on temporal windows ofharvestability for a particular crop at a particular location over somerecent period of time, and comparing this harvestability data against atleast one of time-varying weather data and outputs of models forsimulating plant wetness characteristics in the crop in order toidentify relationships between these external data and the reportedharvestability data. The harvest advisory model 100 then provides, basedon these identified relationships and the at least one of time-varyingweather forecast data and forecast outputs of models for simulatingplant wetness characteristics, a forecast of the likely windows ofharvestability on the current and future days.

In another approach, the present invention predicts the daily window ofharvest opportunity due to the impact of plant wetness on cropthreshability by collecting data on windows of harvestability forvarious crops at various locations, over time, and comparing thisharvestability data against at least one of time-varying weather dataand outputs of models for simulating plant wetness characteristics inthe corresponding times and locations, for the corresponding crops, inorder to develop artificial intelligence models for relating thesewindows of harvestability to the more readily-available weather data andoutputs of models for simulating plant wetness characteristics. Theharvest advisory model 100 then provides, using the artificialintelligence model and at least one of forecast weather data andforecast outputs of models for simulating plant wetness characteristics,a forecast of the likely windows of harvestability on the current andfuture days.

The present invention may also be configured for an application in whichthe harvest condition output profile 182 enables prediction of thedry-down of cutting crops such as hay (and others, for example alfalfa)over time. The harvest advisory tool 200 uses this output of the harvestadvisory model 100 for planning the timing of, or predicting windows ofopportunity for, cutting, windrowing, silageing, or bailing operationsfor hay fields.

The present invention performs this collecting of data on temporalwindows of cut-windrow-bail during consecutive and disjoint operationsfor hay at a particular location over some recent period of time, andcompares this operational data against at least one of time-varyingweather data and outputs of models for simulating plant wetnesscharacteristics in the hay in order to identify relationships betweenthese external data and the reported operational data. The harvestadvisory model 100 then provides, based on these identifiedrelationships and the at least one of time-varying weather forecast dataand forecast outputs of models for simulating plant wetnesscharacteristics, a forecast of the likely windows of, for example,cut-windrow-bail operations on the current and future days.

Additionally, the present invention may also be configured for anapplication in which the harvest condition output profile 182 providesassistance with the decision to apply a desiccant to affect a desiredharvest window. The harvest advisory tool 200 uses this output of theharvest advisory model 100 in one or more approaches. In one suchapproach, the present invention collects data on temporal windows ofmature growth stages, and desiccant efficacy and harvestability for aparticular desiccant for particular crop at a particular location oversome recent period of time. It then compares crop growth stage,desiccant, and harvestability data against at least one of time-varyingweather data and outputs of models for simulating plant wetness and seedmoisture characteristics in the crop in order to identify relationshipsbetween these external data and the reported crop growth stage,desiccant, and harvestability data. The present invention then provides,based on these identified relationships and the at least one oftime-varying weather forecast data and forecast outputs of models forsimulating plant wetness and seed moisture characteristics, a forecastof the likely windows of desiccant efficacy, and subsequently the cropmoisture, on the current and future days.

In another approach, the harvest advisory model 100 collects data ontemporal windows of mature growth stages, desiccant efficacy, andharvestability for a particular desiccant for a particular crop at aparticular location over some recent period of time, and compares cropgrowth stage, desiccant, and harvestability data against at least one oftime-varying weather data and outputs of models for simulating plantwetness and seed moisture characteristics in the crop in order todevelop artificial intelligence models relating these external data andthe reported crop growth stage, desiccant, and harvestability data. Theharvest advisory tool 200 then provides, using the artificialintelligence model and at least one of time-varying weather forecastdata and forecast outputs of models for simulating plant wetness andseed moisture characteristics, a forecast of the likely windows ofdesiccant efficacy, and subsequently the crop moisture, on the currentand future days.

The execution of the harvest advisory model 100 prior to, during, orfollowing harvest activities, addresses multiple factors that can beused by farmers to improve and enhance management of harvest operations.The harvest advisory tool 200 provides farmers with many different typesof harvest-related information, such as evaluations, predictions,forecast, and diagnoses of harvest conditions 270. As noted above, thisharvest-related information may include estimates of standing cropdry-down rates, anticipated harvest dates and suitability, fuelconsumption optimizers for forced-air drying, indicators of plant‘toughness’ for anticipating harvest windows, and possible loss of fieldworkability due to the formation of frost in the soils prior topost-harvest tillage.

One specific example of such harvest-related information includepredictions of expected in-field, pre-harvest dry-down 183 of a maturesmall grain, coarse grain or oilseed crop over time, so that users 300can plan the timing of a harvest operation. Another example is aprediction of time-varying unit costs associated with at least one offuel-based or forced-air mechanical drying 184 of grain resulting fromchanging weather conditions and characteristics of the grain dryingfacility, for post-harvest crop storage. Yet another example is anassessment of expected costs of forced-air or fuel-based mechanicaldrying of a crop owing to early harvest relative to the potential costsof crop losses due to adverse weather conditions and the susceptibilityof the in-field crop to weather and other causes of yield loss. Stillanother example is a diagnosis and prediction of soil temperature andmoisture profiles in a given field, to provide guidance as to when soilsin that field are likely to become exceedingly wet or frozen, therebyinhibiting harvest and post-harvest tillage operations. A furtherexample is a diagnosis and prediction of the impacts of a time-varyingsoil temperature profile in a given field as it pertains to the internaltemperature of root-based crops and their long-term storage stability atthose temperatures, thereby aiding in the timing of harvest operationsso as to optimize storage conditions. Still a further example isprediction of harvest opportunity windows 184, such as the daily windowof harvest opportunity due to the impact of plant wetness on cropthreshability, and the timing or windows of opportunity of cutting,windrowing, silageing, or bailing operations with regard to dry-down ofhay (alfalfa et al.) cutting over time. Additional examples involve adetermination of whether to apply a desiccant 186 to affect a desiredharvest window, and a determination of field workability loss 187.

The harvest advisory tool 200 contemplates that output data 180 may begenerated for visual representation of the information contained, forexample on a graphical user interface. With the harvest advisory tool200, users may be able to configure settings for, and view variousaspects of, the harvest advisory model 100 using a display on suchgraphical user interfaces, and/or via web-based or application-basedmodules. Tools and pull-down menus on such a display (or in web-based orapplication-based modules) may also be provided to customize the typeand nature of the input data 102 applied to the various modelingparadigms of the harvest advisory model 100, as well as the output data180 provided in the harvest condition output profile 182. Examples ofthis include the tactile and/or haptic notifications of changes in theharvest conditions discussed herein. Other types of notifications mayinclude those provided via applications resident on mobile telephony,tablet, or wearable devices are also possible, such as for example avoice-based output generated to verbally notify farmers of possibledisease or pest risk.

As noted above, the output data 180 of the harvest advisory model 100may be used to generate a plurality of advisory services 190 in one ormore application programming interface (API) modules 176. These advisoryservices 190 provide enhanced decision-making support to harvestoperations in precision agricultural production.

These advisory services 190 are driven by the generation of the harvestcondition output profile 182 that results in the harvest actionreporting and harvest summary reporting aspects of the output data 180.Each of these advisory services 190 enriches the utilization andapplication of the augmented crop growth model 100.

One such advisory service 190 is a current and historical weatherreporting service 192. Site-specific weather information is an importantelement of field recordkeeping in agricultural production. Using thefield geo-positional information provided for the harvest advisorymodeling, current and historical weather for the field location may beprovided as an advisory service for specific geographical locations.

A soil modeling service 194 is another advisory service 190 contemplatedwithin the scope of the present invention. One API module 176 appliesthe harvest condition output profile 182 and output data 180 to asophisticated soil model to generate information that provides a betterunderstanding of current and future soil conditions relative tohistorical soil conditions at harvest times. Such a soil model supportsthe use of existing soil properties e.g., organic matter, soil type,etc., tillage practices, presence of tile drainage, and irrigationhistory, along with the advanced short-, medium-, or long-range weatherforecasting. This coupling of modeled soil characteristics with advancedweather forecasting information at harvest times provides a valuabletool for indicating crop potential. The output of an API module 176generating this soil modeling service 194 may be further configured toprovide a detailed past-through-future analysis of soil characteristicsimpacting harvest conditions, such as soil temperature and moistureassessments, including freeze and thaw information, the amount of water‘throughput’, runoff or ponding of soils, and an importantinterpretation of workability of the soils.

Another advisory service 190 is soil conditions forecasting service 196.The soil modeling service 194 can be used with prediction of expectedweather conditions to provide an estimate of future soil conditions atharvest times in this advisory service 190. Using the weather andlocation specifications 110, a forecast of soil temperature and moistureconditions for the specified field location can generated. Such aforecast in this service 196 enables anticipation of periods of suitablesoil conditions for field operations, including workability,particularly those after periods of rainfall.

Another advisory service 190 as an output 180 of the harvest advisorymodel 100 is a harvest alerts service 198. The importance of informationprovided to production agriculture in the present invention dictates anecessity for an effective and direct method of conveying information.The harvest alerts service 198 may utilize a ‘push’ technology forimmediate and direct dissemination of information provided as outputdata 180 from the harvest advisory model 100. The API module 176 thatgenerates this service may be configured to so that as the time forconducting a harvest operation nears, users may receive alerts ofimportant combinations of weather conditions, risk factors, anddecision-support aides for the purposes of managing and timing harvestoperations. This is supported by user-specified elements including alertparameters, location(s), lead time(s), time(s) of day, and theparticular device(s) to receive the alerts. For example, a customharvesting operation may integrate their work orders with such analerting system in order to optimize the logistics of their operation byavoiding costly weather-related delays to harvest operations, througheither spatial or temporal reassignment of equipment and relatedresources.

As noted above, the present invention may in one embodiment isconfigured to diagnosis and prediction of the impacts of a time-varyingsoil temperature profile in a given field as it pertains to the internaltemperature of root-based crops and their long-term storage stability atthose temperatures. Such a model aids in the timing of harvestoperations so as to optimize crop storage conditions.

Such an embodiment is performed in a system and method of evaluating atiming of harvest operations and crop storage conditions, comprising inwhich a profile of long-term storability of an agricultural commodity isdeveloped with a harvest advisory model. The harvest advisory model inthis embodiment predicts expected weather conditions relative to atiming of harvest operations and crop storage conditions by applyingweather information including recent and current field-level weatherdata and extended-range weather forecast data, and field-specificinformation including location data representing a field where anagricultural commodity is planted to one or more predictive numericalweather models. The harvest advisory model forecasts harvestabilitywindows over a specified period of time and crop stability for long-termstorage, by simulating the expected weather conditions, crop-specificinformation for an agricultural commodity to be harvested, and thefield-specific information in an agricultural model of one or morephysical and empirical characteristics of crop moisture and croptemperature for harvestability of crops at corresponding times andlocations. The agricultural model comprises at least one of acrop-specific growing degree day model, a soil model, a land surfacemodel. Also, an artificial intelligence model may be developed tosimulate crop storage characteristics from the expected weatherconditions, one or more sampled observations from a planted field, andcrop storage facility data by building a comprehensive dataset for theagricultural model of one or more physical and empirical characteristicsof crop moisture and crop temperature for harvestability of crops atcorresponding times and locations.

The systems and methods of the present invention may be implemented inmany different computing environments 150. For example, they may beimplemented in conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, electronic or logic circuitry such as discrete elementcircuit, a programmable logic device or gate array such as a PLD, PLA,FPGA, PAL, and any comparable means. In general, any means ofimplementing the methodology illustrated herein can be used to implementthe various aspects of the present invention. Exemplary hardware thatcan be used for the present invention includes computers, handhelddevices, telephones (e.g., cellular, Internet enabled, digital, analog,hybrids, and others), and other such hardware. Some of these devicesinclude processors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing, parallel processing, or virtual machineprocessing can also be configured to perform the methods describedherein.

The systems and methods of the present invention may also be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this inventioncan be implemented as a program embedded on a mobile device or personalcomputer through such mediums as an applet, JAVA® or CGI script, as aresource residing on one or more servers or computer workstations, as aroutine embedded in a dedicated measurement system, system component, orthe like. The system can also be implemented by physically incorporatingthe system and/or method into a software and/or hardware system.

Additionally, the data processing functions disclosed herein may beperformed by one or more program instructions stored in or executed bysuch memory, and further may be performed by one or more modulesconfigured to carry out those program instructions. Modules are intendedto refer to any known or later developed hardware, software, firmware,artificial intelligence, fuzzy logic, expert system or combination ofhardware and software that is capable of performing the data processingfunctionality described herein.

The foregoing descriptions of embodiments of the present invention havebeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Accordingly, many alterations, modifications andvariations are possible in light of the above teachings, may be made bythose having ordinary skill in the art without departing from the spiritand scope of the invention. It is therefore intended that the scope ofthe invention be limited not by this detailed description. For example,notwithstanding the fact that the elements of a claim are set forthbelow in a certain combination, it must be expressly understood that theinvention includes other combinations of fewer, more or differentelements, which are disclosed in above even when not initially claimedin such combinations.

The words used in this specification to describe the invention and itsvarious embodiments are to be understood not only in the sense of theircommonly defined meanings, but to include by special definition in thisspecification structure, material or acts beyond the scope of thecommonly defined meanings. Thus if an element can be understood in thecontext of this specification as including more than one meaning, thenits use in a claim must be understood as being generic to all possiblemeanings supported by the specification and by the word itself.

The definitions of the words or elements of the following claims are,therefore, defined in this specification to include not only thecombination of elements which are literally set forth, but allequivalent structure, material or acts for performing substantially thesame function in substantially the same way to obtain substantially thesame result. In this sense it is therefore contemplated that anequivalent substitution of two or more elements may be made for any oneof the elements in the claims below or that a single element may besubstituted for two or more elements in a claim. Although elements maybe described above as acting in certain combinations and even initiallyclaimed as such, it is to be expressly understood that one or moreelements from a claimed combination can in some cases be excised fromthe combination and that the claimed combination may be directed to asub-combination or variation of a sub-combination.

Insubstantial changes from the claimed subject matter as viewed by aperson with ordinary skill in the art, now known or later devised, areexpressly contemplated as being equivalently within the scope of theclaims. Therefore, obvious substitutions now or later known to one withordinary skill in the art are defined to be within the scope of thedefined elements.

The claims are thus to be understood to include what is specificallyillustrated and described above, what is conceptually equivalent, whatcan be obviously substituted and also what essentially incorporates theessential idea of the invention.

The invention claimed is:
 1. A method comprising: ingesting, as inputdata, weather information and crop-specific information for a foodgrains crop to be harvested, the weather information including recentand current field-level weather data and extended-range weather forecastdata, and the crop-specific information including at least one of croptype data and planting data; modeling the input data in a plurality ofdata processing modules within a computing environment in which theplurality of data processing modules are executed in conjunction with atleast one processor, the data processing modules configured to profilein-field dry-down of the food grains crop for planning a timing ofharvest operations by 1) predicting expected weather conditionsimpacting the rate of drying of the food grains crop in the plantedfield at a specific location, 2) applying the expected weatherconditions and the crop-specific information to an agricultural model ofone or more physical and empirical characteristics impacting harvestoperations of the food grains crop to simulate an expected rate ofdrying of the food grains crop in a particular field, and 3) identifyingdifferences in the simulation of expected rate of drying with one ormore sampled observations of actual moisture from a planted field atcorresponding times, and modifying the simulation based on thedifferences between the otherwise simulated rate of drying and the oneor more sampled observations of actual moisture content at thecorresponding times to predict an in-field dry-down of the food grainscrop in the particular field over time; and generating, as output data,one or more advisories representing the predicted in-field dry-down in aharvest output condition profile.
 2. The method of claim 1, furthercomprising applying the harvest output condition profile to a harvestadvisory tool configured to provide the one or more advisoriesrepresenting the predicted in-field dry-down to a user.
 3. The method ofclaim 1, wherein the crop-specific information further includes one ormore of crop relative maturity data, crop planting depth and row spacingdata, crop post-maturity dry-down characteristics, and targeted harvestcrop moisture or temperature thresholds for the food grains crop.
 4. Themethod of claim 1, wherein the modeling further comprises applying theweather information to one or more predictive numerical weather modelsto generate the prediction of expected weather conditions.
 5. The methodof claim 1, wherein the agricultural model of one or more physical andempirical characteristics impacting harvest operations of the maturesmall grain, coarse grain, or oilseed crop includes at least one of acrop-specific growth model, an in-field dry-down model, a soil model,and a land surface model.
 6. The method of claim 1, wherein the one ormore sampled observations of actual moisture from a planted field atcorresponding times includes physical, empirical or observedagricultural information and field and soil information that comprisessampled grain moisture content.
 7. The method of claim 1, furthercomprising applying the expected weather conditions, the one or moresamples of actual moisture from a planted field at corresponding times,and crop metadata representing actual and/or realized performance of thefood grains crop over a specified period of time to automaticallydevelop an artificial intelligence model of in-field grain dry-down bybuilding a comprehensive harvest condition dataset for the agriculturalmodel of one or more physical and empirical characteristics impactingharvest operations of the food grains crop to predict the in-fielddry-down of a crop with similar characteristics in any field at anyselected time.
 8. The method of claim 1, wherein the food grains crop isat least one of a mature small grain, coarse grain, or oilseed crop. 9.A system comprising: a computing environment including at least onecomputer-readable storage medium having program instructions storedtherein and a computer processor operable to execute the programinstructions to profile in-field dry-down of a food grains crop forplanning a timing of harvest operations by performing a harvest advisorymodel within a plurality of data processing modules, the plurality ofdata processing modules including: a weather modeling module configuredto predict expected weather conditions at a specific location byapplying weather information including recent and current field-levelweather data and extended-range weather forecast data to one or morepredictive numerical weather models; one or more modules configuredto 1) aggregate the expected weather conditions with crop-specificinformation into an agricultural model of one or more physical andempirical characteristics impacting a rate of drying, configured tosimulate an expected rate of drying of the food grains crop in aparticular field, and 2) automatically develop an artificialintelligence model configured to analyze a specific in-field dry-downmodel by building a comprehensive dataset for the agricultural model ofone or more physical and empirical characteristics impacting harvestoperations of the food grains crop, and predict in-field dry-down of acrop with similar characteristics in any field at any selected time; anda harvest condition prediction module configured to predict in-fielddry-down of the food grains crop generate one or more advisories of thepredicted in-field dry-down in a harvest output condition profile. 10.The system of claim 9, wherein the comprehensive dataset for theagricultural model of one or more physical and empirical characteristicsimpacting harvest operations of the food grains crop is built byaggregating crop metadata representing actual and/or realizedperformance of the food grains crop over a specified period of time withone or more sampled observations of actual moisture from one or morefields and one or more crops.
 11. The system of claim 9, wherein the oneor more sampled observations of actual moisture from a planted field atcorresponding times includes physical, empirical or observedagricultural information and field and soil information that comprisessampled grain moisture content.
 12. The system of claim 9, wherein theharvest output condition profile is applied to a diagnostic support toolconfigured to provide the one or more advisories representing thepredicted in-field dry-down to a user.
 13. The system of claim 9,wherein the one or more modules includes a physical and empiricalmodeling module, within which differences in the simulation of expectedrate of drying with one or more sampled observations of actual moisturefrom a planted field at corresponding times are identified, wherein theharvest advisory model performs a modification of the simulation of theexpected rate of drying based on the differences.
 14. The system ofclaim 9, wherein the crop-specific information further includes one ormore of crop relative maturity data, crop planting depth and row spacingdata, crop post-maturity dry-down characteristics, and targeted harvestcrop moisture or temperature thresholds.
 15. A method of evaluatingin-field dry-down of a mature small grain, coarse grain, or oilseedcrop, comprising: within a computing environment comprised of a computerprocessor and at least one computer-readable storage medium operablycoupled to the computer processor and having program instructions storedtherein, the computer processor being operable to execute the programinstructions to profile in-field dry-down of a food grains crop in aharvest advisory model configured to perform the steps of: predictingexpected weather conditions impacting a rate of drying of the grainassociated with the food grains crop at a specific location by applyingweather information comprised of recent and current field-level weatherdata and extended-range weather forecast data to one or more predictivenumerical weather models; simulating an expected rate of drying of thefood grains crop in a particular field by applying the expected weatherconditions, crop-specific information including crop type data, relativematurity data, planting data, and harvest data, and one or more sampledobservations of actual moisture from a planted field at one or moretimes to an agricultural model of one or more physical and empiricalcharacteristics impacting harvest operations of the food grains crop,the agricultural model comprising at least one of a crop-specific growthmodel, an in-field dry-down model, a soil model, and a land surfacemodel; and modifying the simulation of the expected rate of drying usingdifferences between the otherwise simulated expected rate of drying andthe one or more sampled observations of actual moisture from a plantedfield at corresponding times, to generate a prediction of pre-in-fielddry-down of the food grains crop in the particular field over time. 16.The method of claim 15, further comprising generating one or moreadvisories of the predicted in-field dry-down in a harvest outputcondition profile.
 17. The method of claim 16, further comprisingapplying the harvest output condition profile to a diagnostic supporttool configured to provide the one or more advisories representing thepredicted in-field dry-down to users performing harvest operations. 18.The method of claim 15, wherein the crop-specific information furtherincludes one or more of crop relative maturity data, crop planting depthand row spacing data, crop post-maturity dry-down characteristics, andtargeted harvest crop moisture or temperature thresholds.
 19. The methodof claim 15, further comprising automatically developing or moreartificial intelligence models configured to analyze a specific in-fieldgrain dry-down by building a comprehensive harvest condition dataset forthe agricultural model of one or more physical and empiricalcharacteristics impacting harvest operations.
 20. The method of claim15, wherein the one or more sampled observations of actual moisture froma planted field at corresponding times includes physical, empirical orobserved agricultural information and field and soil information thatcomprises sampled grain moisture content.
 21. A method of evaluatingin-field dry-down of a food grains crop, comprising: within a computingenvironment comprised of a computer processor and at least onecomputer-readable storage medium operably coupled to the computerprocessor and having program instructions stored therein, the computerprocessor being operable to execute the program instructions to profilein-field dry-down of the food grains crop in a harvest advisory modelconfigured to perform the steps of: predicting expected weatherconditions impacting a rate of drying of the grain associated with foodgrains crop at a specific location by applying weather informationcomprised of recent or current field-level weather data andextended-range weather forecast data to one or more predictive numericalweather models; automatically developing an artificial intelligencemodel configured to analyze a specific in-field dry-down by applying theexpected weather conditions, crop metadata representing actual and/orrealized performance of the food grains crop over a specified period oftime, and one or more sampled observations of actual moisture from oneor more fields and one or more crops, by building a comprehensivedataset for the agricultural model of one or more physical and empiricalcharacteristics impacting harvest operations of the food grains crop topredict the in-field dry-down of a crop with similar characteristics inany field at any selected time.
 22. The method of claim 21, furthercomprising generating one or more advisories of the predicted in-fielddry-down in a harvest output condition profile.
 23. The method of claim22, further comprising applying the harvest output condition profile toa diagnostic support tool configured to provide the one or moreadvisories representing the predicted pre-harvest in-field dry-down tousers performing harvest operations.
 24. The method of claim 22, furthercomprising applying crop-specific information to the artificialintelligence model, the crop-specific information including one or moreof crop relative maturity data, crop planting depth and row spacingdata, crop post-maturity dry-down characteristics, and targeted harvestcrop moisture or temperature thresholds.
 25. The method of claim 24,wherein the one or more sampled observations of actual moisture from aplanted field at corresponding times includes physical, empirical orobserved agricultural information and field and soil information thatcomprises sampled grain moisture content.